#!/usr/bin/env python import numpy as np import cv2 as cv import os import sys import unittest from tests_common import NewOpenCVTests try: if sys.version_info[:2] < (3, 0): raise unittest.SkipTest('Python 2.x is not supported') class test_gapi_infer(NewOpenCVTests): def infer_reference_network(self, model_path, weights_path, img): net = cv.dnn.readNetFromModelOptimizer(model_path, weights_path) net.setPreferableBackend(cv.dnn.DNN_BACKEND_INFERENCE_ENGINE) net.setPreferableTarget(cv.dnn.DNN_TARGET_CPU) blob = cv.dnn.blobFromImage(img) net.setInput(blob) return net.forward(net.getUnconnectedOutLayersNames()) def make_roi(self, img, roi): return img[roi[1]:roi[1] + roi[3], roi[0]:roi[0] + roi[2], ...] def test_age_gender_infer(self): # NB: Check IE if not cv.dnn.DNN_TARGET_CPU in cv.dnn.getAvailableTargets(cv.dnn.DNN_BACKEND_INFERENCE_ENGINE): return root_path = '/omz_intel_models/intel/age-gender-recognition-retail-0013/FP32/age-gender-recognition-retail-0013' model_path = self.find_file(root_path + '.xml', [os.environ.get('OPENCV_DNN_TEST_DATA_PATH')]) weights_path = self.find_file(root_path + '.bin', [os.environ.get('OPENCV_DNN_TEST_DATA_PATH')]) device_id = 'CPU' img_path = self.find_file('cv/face/david2.jpg', [os.environ.get('OPENCV_TEST_DATA_PATH')]) img = cv.resize(cv.imread(img_path), (62,62)) # OpenCV DNN dnn_age, dnn_gender = self.infer_reference_network(model_path, weights_path, img) # OpenCV G-API g_in = cv.GMat() inputs = cv.GInferInputs() inputs.setInput('data', g_in) outputs = cv.gapi.infer("net", inputs) age_g = outputs.at("age_conv3") gender_g = outputs.at("prob") comp = cv.GComputation(cv.GIn(g_in), cv.GOut(age_g, gender_g)) pp = cv.gapi.ie.params("net", model_path, weights_path, device_id) gapi_age, gapi_gender = comp.apply(cv.gin(img), args=cv.gapi.compile_args(cv.gapi.networks(pp))) # Check self.assertEqual(0.0, cv.norm(dnn_gender, gapi_gender, cv.NORM_INF)) self.assertEqual(0.0, cv.norm(dnn_age, gapi_age, cv.NORM_INF)) def test_age_gender_infer_roi(self): # NB: Check IE if not cv.dnn.DNN_TARGET_CPU in cv.dnn.getAvailableTargets(cv.dnn.DNN_BACKEND_INFERENCE_ENGINE): return root_path = '/omz_intel_models/intel/age-gender-recognition-retail-0013/FP32/age-gender-recognition-retail-0013' model_path = self.find_file(root_path + '.xml', [os.environ.get('OPENCV_DNN_TEST_DATA_PATH')]) weights_path = self.find_file(root_path + '.bin', [os.environ.get('OPENCV_DNN_TEST_DATA_PATH')]) device_id = 'CPU' img_path = self.find_file('cv/face/david2.jpg', [os.environ.get('OPENCV_TEST_DATA_PATH')]) img = cv.imread(img_path) roi = (10, 10, 62, 62) # OpenCV DNN dnn_age, dnn_gender = self.infer_reference_network(model_path, weights_path, self.make_roi(img, roi)) # OpenCV G-API g_in = cv.GMat() g_roi = cv.GOpaqueT(cv.gapi.CV_RECT) inputs = cv.GInferInputs() inputs.setInput('data', g_in) outputs = cv.gapi.infer("net", g_roi, inputs) age_g = outputs.at("age_conv3") gender_g = outputs.at("prob") comp = cv.GComputation(cv.GIn(g_in, g_roi), cv.GOut(age_g, gender_g)) pp = cv.gapi.ie.params("net", model_path, weights_path, device_id) gapi_age, gapi_gender = comp.apply(cv.gin(img, roi), args=cv.gapi.compile_args(cv.gapi.networks(pp))) # Check self.assertEqual(0.0, cv.norm(dnn_gender, gapi_gender, cv.NORM_INF)) self.assertEqual(0.0, cv.norm(dnn_age, gapi_age, cv.NORM_INF)) def test_age_gender_infer_roi_list(self): # NB: Check IE if not cv.dnn.DNN_TARGET_CPU in cv.dnn.getAvailableTargets(cv.dnn.DNN_BACKEND_INFERENCE_ENGINE): return root_path = '/omz_intel_models/intel/age-gender-recognition-retail-0013/FP32/age-gender-recognition-retail-0013' model_path = self.find_file(root_path + '.xml', [os.environ.get('OPENCV_DNN_TEST_DATA_PATH')]) weights_path = self.find_file(root_path + '.bin', [os.environ.get('OPENCV_DNN_TEST_DATA_PATH')]) device_id = 'CPU' rois = [(10, 15, 62, 62), (23, 50, 62, 62), (14, 100, 62, 62), (80, 50, 62, 62)] img_path = self.find_file('cv/face/david2.jpg', [os.environ.get('OPENCV_TEST_DATA_PATH')]) img = cv.imread(img_path) # OpenCV DNN dnn_age_list = [] dnn_gender_list = [] for roi in rois: age, gender = self.infer_reference_network(model_path, weights_path, self.make_roi(img, roi)) dnn_age_list.append(age) dnn_gender_list.append(gender) # OpenCV G-API g_in = cv.GMat() g_rois = cv.GArrayT(cv.gapi.CV_RECT) inputs = cv.GInferInputs() inputs.setInput('data', g_in) outputs = cv.gapi.infer("net", g_rois, inputs) age_g = outputs.at("age_conv3") gender_g = outputs.at("prob") comp = cv.GComputation(cv.GIn(g_in, g_rois), cv.GOut(age_g, gender_g)) pp = cv.gapi.ie.params("net", model_path, weights_path, device_id) gapi_age_list, gapi_gender_list = comp.apply(cv.gin(img, rois), args=cv.gapi.compile_args(cv.gapi.networks(pp))) # Check for gapi_age, gapi_gender, dnn_age, dnn_gender in zip(gapi_age_list, gapi_gender_list, dnn_age_list, dnn_gender_list): self.assertEqual(0.0, cv.norm(dnn_gender, gapi_gender, cv.NORM_INF)) self.assertEqual(0.0, cv.norm(dnn_age, gapi_age, cv.NORM_INF)) def test_age_gender_infer2_roi(self): # NB: Check IE if not cv.dnn.DNN_TARGET_CPU in cv.dnn.getAvailableTargets(cv.dnn.DNN_BACKEND_INFERENCE_ENGINE): return root_path = '/omz_intel_models/intel/age-gender-recognition-retail-0013/FP32/age-gender-recognition-retail-0013' model_path = self.find_file(root_path + '.xml', [os.environ.get('OPENCV_DNN_TEST_DATA_PATH')]) weights_path = self.find_file(root_path + '.bin', [os.environ.get('OPENCV_DNN_TEST_DATA_PATH')]) device_id = 'CPU' rois = [(10, 15, 62, 62), (23, 50, 62, 62), (14, 100, 62, 62), (80, 50, 62, 62)] img_path = self.find_file('cv/face/david2.jpg', [os.environ.get('OPENCV_TEST_DATA_PATH')]) img = cv.imread(img_path) # OpenCV DNN dnn_age_list = [] dnn_gender_list = [] for roi in rois: age, gender = self.infer_reference_network(model_path, weights_path, self.make_roi(img, roi)) dnn_age_list.append(age) dnn_gender_list.append(gender) # OpenCV G-API g_in = cv.GMat() g_rois = cv.GArrayT(cv.gapi.CV_RECT) inputs = cv.GInferListInputs() inputs.setInput('data', g_rois) outputs = cv.gapi.infer2("net", g_in, inputs) age_g = outputs.at("age_conv3") gender_g = outputs.at("prob") comp = cv.GComputation(cv.GIn(g_in, g_rois), cv.GOut(age_g, gender_g)) pp = cv.gapi.ie.params("net", model_path, weights_path, device_id) gapi_age_list, gapi_gender_list = comp.apply(cv.gin(img, rois), args=cv.gapi.compile_args(cv.gapi.networks(pp))) # Check for gapi_age, gapi_gender, dnn_age, dnn_gender in zip(gapi_age_list, gapi_gender_list, dnn_age_list, dnn_gender_list): self.assertEqual(0.0, cv.norm(dnn_gender, gapi_gender, cv.NORM_INF)) self.assertEqual(0.0, cv.norm(dnn_age, gapi_age, cv.NORM_INF)) def test_person_detection_retail_0013(self): # NB: Check IE if not cv.dnn.DNN_TARGET_CPU in cv.dnn.getAvailableTargets(cv.dnn.DNN_BACKEND_INFERENCE_ENGINE): return root_path = '/omz_intel_models/intel/person-detection-retail-0013/FP32/person-detection-retail-0013' model_path = self.find_file(root_path + '.xml', [os.environ.get('OPENCV_DNN_TEST_DATA_PATH')]) weights_path = self.find_file(root_path + '.bin', [os.environ.get('OPENCV_DNN_TEST_DATA_PATH')]) img_path = self.find_file('gpu/lbpcascade/er.png', [os.environ.get('OPENCV_TEST_DATA_PATH')]) device_id = 'CPU' img = cv.resize(cv.imread(img_path), (544, 320)) # OpenCV DNN net = cv.dnn.readNetFromModelOptimizer(model_path, weights_path) net.setPreferableBackend(cv.dnn.DNN_BACKEND_INFERENCE_ENGINE) net.setPreferableTarget(cv.dnn.DNN_TARGET_CPU) blob = cv.dnn.blobFromImage(img) def parseSSD(detections, size): h, w = size bboxes = [] detections = detections.reshape(-1, 7) for sample_id, class_id, confidence, xmin, ymin, xmax, ymax in detections: if confidence >= 0.5: x = int(xmin * w) y = int(ymin * h) width = int(xmax * w - x) height = int(ymax * h - y) bboxes.append((x, y, width, height)) return bboxes net.setInput(blob) dnn_detections = net.forward() dnn_boxes = parseSSD(np.array(dnn_detections), img.shape[:2]) # OpenCV G-API g_in = cv.GMat() inputs = cv.GInferInputs() inputs.setInput('data', g_in) g_sz = cv.gapi.streaming.size(g_in) outputs = cv.gapi.infer("net", inputs) detections = outputs.at("detection_out") bboxes = cv.gapi.parseSSD(detections, g_sz, 0.5, False, False) comp = cv.GComputation(cv.GIn(g_in), cv.GOut(bboxes)) pp = cv.gapi.ie.params("net", model_path, weights_path, device_id) gapi_boxes = comp.apply(cv.gin(img.astype(np.float32)), args=cv.gapi.compile_args(cv.gapi.networks(pp))) # Comparison self.assertEqual(0.0, cv.norm(np.array(dnn_boxes).flatten(), np.array(gapi_boxes).flatten(), cv.NORM_INF)) def test_person_detection_retail_0013(self): # NB: Check IE if not cv.dnn.DNN_TARGET_CPU in cv.dnn.getAvailableTargets(cv.dnn.DNN_BACKEND_INFERENCE_ENGINE): return root_path = '/omz_intel_models/intel/person-detection-retail-0013/FP32/person-detection-retail-0013' model_path = self.find_file(root_path + '.xml', [os.environ.get('OPENCV_DNN_TEST_DATA_PATH')]) weights_path = self.find_file(root_path + '.bin', [os.environ.get('OPENCV_DNN_TEST_DATA_PATH')]) img_path = self.find_file('gpu/lbpcascade/er.png', [os.environ.get('OPENCV_TEST_DATA_PATH')]) device_id = 'CPU' img = cv.resize(cv.imread(img_path), (544, 320)) # OpenCV DNN net = cv.dnn.readNetFromModelOptimizer(model_path, weights_path) net.setPreferableBackend(cv.dnn.DNN_BACKEND_INFERENCE_ENGINE) net.setPreferableTarget(cv.dnn.DNN_TARGET_CPU) blob = cv.dnn.blobFromImage(img) def parseSSD(detections, size): h, w = size bboxes = [] detections = detections.reshape(-1, 7) for sample_id, class_id, confidence, xmin, ymin, xmax, ymax in detections: if confidence >= 0.5: x = int(xmin * w) y = int(ymin * h) width = int(xmax * w - x) height = int(ymax * h - y) bboxes.append((x, y, width, height)) return bboxes net.setInput(blob) dnn_detections = net.forward() dnn_boxes = parseSSD(np.array(dnn_detections), img.shape[:2]) # OpenCV G-API g_in = cv.GMat() inputs = cv.GInferInputs() inputs.setInput('data', g_in) g_sz = cv.gapi.streaming.size(g_in) outputs = cv.gapi.infer("net", inputs) detections = outputs.at("detection_out") bboxes = cv.gapi.parseSSD(detections, g_sz, 0.5, False, False) comp = cv.GComputation(cv.GIn(g_in), cv.GOut(bboxes)) pp = cv.gapi.ie.params("net", model_path, weights_path, device_id) gapi_boxes = comp.apply(cv.gin(img.astype(np.float32)), args=cv.gapi.compile_args(cv.gapi.networks(pp))) # Comparison self.assertEqual(0.0, cv.norm(np.array(dnn_boxes).flatten(), np.array(gapi_boxes).flatten(), cv.NORM_INF)) except unittest.SkipTest as e: message = str(e) class TestSkip(unittest.TestCase): def setUp(self): self.skipTest('Skip tests: ' + message) def test_skip(): pass pass if __name__ == '__main__': NewOpenCVTests.bootstrap()